Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference
Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality. MedNLI is a biomedical flavour of NLI for clinical domain. This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI. The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMICIII v1.4, achieves state of the art results on MedNLI (83.45{\%}) and an accuracy of 78.5{\%} in MEDIQA challenge. The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz.
Code (0)
등록된 구현이 없습니다.
Tasks
Natural Language InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the…
Question AnsweringRelationDUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBE…
Medical Question AnsweringMulti-Task LearningQuestion AnsweringNCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model
This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop. We use the BERT (Bidirectional Encoder Representations from Transformers) as the word embedding method t…
Natural Language InferenceBioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical liter…
Drug–drug Interaction ExtractionFew-Shot LearningLanguage ModellingMedical Named Entity Recognition+8MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning
The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by pr…
Answer GenerationMedical Question AnsweringQuestion AnsweringWeakly-supervised Learning